Gas Cost Estimator: estimation of gas costs using the measure marginal method
In this script, there is conducted the estimation for the
measure_marginal approach for a single given env. The
programs is the set of bytecode programs with varying number of opcodes.
The measurements are time measurements of program runs or benchmarks.
The estimation of each opcode is calculated as the regression against
the number of a given opcode in the executed programs.
Parametrization. The evm client name env=geth, the file with programs
programs=pg_marginal_full_step5_v2.csv, the file with measurements
results=results_marginal_full_geth.csv,
the output csv file with estimated cost
output_estimated_cost=reports-08.11.2024/estimated_cost_geth_full.csv,
should the details be included in the report details=TRUE.
Data preparation. Reading data from the programs file and results file. Initial adjustments.
# The example of programs file: pg_marginal_full5_c50_step1_shuffle.csv .
# The example of results file: geth_pg_marginal_full5_c50_step1_shuffle_50_4.csv
programs = read.csv(params$programs)
results = load_data_set_from_file(params$results)
if(!("run_id" %in% colnames(results))) {
results$run_id <- 1
}
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "total_time_ns")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$total_time_ns != 0), ]
if (class(results[,"total_time_ns"]) == "character") {
stop("at least one of 'total_time_ns' value cannot be parsed into numeric type")
}measurements = sqldf(paste0("SELECT opcode, op_count, sample_id, run_id, total_time_ns as measure_total_time_ns, '", env, "' as env, results.program_id
FROM results
INNER JOIN
programs ON(results.program_id = programs.program_id)"))
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
# head(measurements)
extract_opcodes <- function() {
unique(measurements$opcode)
}
all_opcodes = extract_opcodes()Measurement point distribution. For bare eye assessment. Every point is a sinle measurement. For each opcode and op_count, the measurements should tend to be concentrated around a single value.
for (opcode in all_opcodes) {
df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
plot(measure_total_time_ns ~ op_count, data=df, las=2)
title(main=paste(env, opcode, "- measurement point distribution"))
}
The comparision of result. Before and after removing outlying
measurement. Switch removed_outliers to FALSE
to see the comparison.
if (removed_outliers) {
measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', c(env))
}
# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, use_median) {
if (missing(plots)) {
plots = "scatter"
}
if (missing(use_median)) {
use_median = FALSE
}
if (plots == "all") {
print(c(opcode, env))
}
df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
if (use_median) {
f = median
} else {
f = mean
}
df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
step_=max(df_mean$op_count)/(nrow(df_mean)-1)
model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
model_mean_summary = summary(model_mean)
if (plots == "diagnostics" | plots == "all") {
print(model_mean_summary)
}
slope = model_mean_summary$coefficients['op_count','Estimate']
intercept = model_mean_summary$coefficients[1,'Estimate']
stderr = model_mean_summary$coefficients['op_count','Std. Error']
if (plots == "scatter" | plots == "all") {
par(mfrow=c(1,1))
boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
rounded_slope = round(slope, 3)
rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
rounded_stderr = round(stderr, 3)
title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
abline(a=intercept-slope*step_, b=slope*step_, col="red")
}
if (plots == "diagnostics" | plots == "all") {
par(mfrow=c(2,2))
plot(model_mean)
}
list("slope" = slope, "stderr" = stderr)
}# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not be an issue.
for (opcode in all_opcodes) {
boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}
Now we can investigate the linear regressions.
for (opcode in all_opcodes) {
estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, plots=ifelse(details,'all','scatter'))
estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}## [1] "ADD" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -333.77 -133.09 49.32 104.31 367.41
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8064.614 118.501 68.06 0.000000000000161 ***
## op_count -51.441 4.006 -12.84 0.000000431613844 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 210.1 on 9 degrees of freedom
## Multiple R-squared: 0.9482, Adjusted R-squared: 0.9425
## F-statistic: 164.9 on 1 and 9 DF, p-value: 0.0000004316
## [1] "MUL" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -82.386 -34.011 5.795 32.000 70.136
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4975.1591 29.2904 169.86 < 0.0000000000000002 ***
## op_count 13.0045 0.9902 13.13 0.000000356 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 51.93 on 9 degrees of freedom
## Multiple R-squared: 0.9504, Adjusted R-squared: 0.9449
## F-statistic: 172.5 on 1 and 9 DF, p-value: 0.0000003557
## [1] "SUB" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -148.52 -48.53 -38.41 33.23 173.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5136.523 59.268 86.667 0.0000000000000184 ***
## op_count 5.245 2.004 2.618 0.0279 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 105.1 on 9 degrees of freedom
## Multiple R-squared: 0.4322, Adjusted R-squared: 0.3691
## F-statistic: 6.852 on 1 and 9 DF, p-value: 0.02792
## [1] "DIV" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.2500 -6.7864 0.6545 5.3250 17.7182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4987.2500 5.4358 917.48 < 0.0000000000000002 ***
## op_count 7.8064 0.1838 42.48 0.0000000000111 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.637 on 9 degrees of freedom
## Multiple R-squared: 0.995, Adjusted R-squared: 0.9945
## F-statistic: 1805 on 1 and 9 DF, p-value: 0.00000000001108
## [1] "SDIV" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1364 -2.6864 0.2636 5.7136 7.9636
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4989.1364 4.6016 1084.21 < 0.0000000000000002 ***
## op_count 9.5800 0.1556 61.58 0.000000000000396 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.158 on 9 degrees of freedom
## Multiple R-squared: 0.9976, Adjusted R-squared: 0.9974
## F-statistic: 3792 on 1 and 9 DF, p-value: 0.0000000000003958
## [1] "MOD" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.091 -5.032 2.582 3.750 21.855
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4986.0909 7.3166 681.47 < 0.0000000000000002 ***
## op_count 7.4109 0.2473 29.96 0.000000000251 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.97 on 9 degrees of freedom
## Multiple R-squared: 0.9901, Adjusted R-squared: 0.989
## F-statistic: 897.7 on 1 and 9 DF, p-value: 0.0000000002512
## [1] "SMOD" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.568 -5.321 2.468 5.334 7.623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4978.5682 4.1857 1189.42 < 0.0000000000000002 ***
## op_count 9.3155 0.1415 65.83 0.000000000000217 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.42 on 9 degrees of freedom
## Multiple R-squared: 0.9979, Adjusted R-squared: 0.9977
## F-statistic: 4334 on 1 and 9 DF, p-value: 0.0000000000002174
## [1] "ADDMOD" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.500 -7.436 2.427 7.600 20.136
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4988.0000 7.7067 647.23 < 0.0000000000000002 ***
## op_count 12.6145 0.2605 48.42 0.00000000000343 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.66 on 9 degrees of freedom
## Multiple R-squared: 0.9962, Adjusted R-squared: 0.9958
## F-statistic: 2344 on 1 and 9 DF, p-value: 0.000000000003428
## [1] "MULMOD" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.932 -2.618 3.805 9.041 12.936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4982.9318 8.2442 604.42 < 0.0000000000000002 ***
## op_count 22.4264 0.2787 80.47 0.0000000000000358 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.62 on 9 degrees of freedom
## Multiple R-squared: 0.9986, Adjusted R-squared: 0.9985
## F-statistic: 6475 on 1 and 9 DF, p-value: 0.0000000000000358
## [1] "EXP" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.273 -5.691 2.491 13.364 19.236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5005.2727 11.4545 436.97 < 0.0000000000000002 ***
## op_count 26.9745 0.3872 69.66 0.000000000000131 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.31 on 9 degrees of freedom
## Multiple R-squared: 0.9981, Adjusted R-squared: 0.9979
## F-statistic: 4852 on 1 and 9 DF, p-value: 0.0000000000001308
## [1] "SIGNEXTEND" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.9091 -6.8364 0.1636 4.2273 20.1455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4971.727 5.708 871.00 < 0.0000000000000002 ***
## op_count 9.004 0.193 46.66 0.00000000000478 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.12 on 9 degrees of freedom
## Multiple R-squared: 0.9959, Adjusted R-squared: 0.9954
## F-statistic: 2177 on 1 and 9 DF, p-value: 0.000000000004777
## [1] "LT" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.523 -4.875 2.796 5.216 12.864
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4981.5227 5.5586 896.17 < 0.0000000000000002 ***
## op_count 7.3227 0.1879 38.97 0.000000000024 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.854 on 9 degrees of freedom
## Multiple R-squared: 0.9941, Adjusted R-squared: 0.9935
## F-statistic: 1519 on 1 and 9 DF, p-value: 0.00000000002399
## [1] "GT" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.0227 -2.7977 -0.9227 5.4773 13.7273
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4977.023 5.947 836.88 < 0.0000000000000002 ***
## op_count 7.390 0.201 36.76 0.0000000000405 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.54 on 9 degrees of freedom
## Multiple R-squared: 0.9934, Adjusted R-squared: 0.9926
## F-statistic: 1351 on 1 and 9 DF, p-value: 0.00000000004046
## [1] "SLT" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -104.773 -29.545 -3.318 19.314 132.473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4952.318 34.867 142.033 < 0.0000000000000002 ***
## op_count 10.649 1.179 9.034 0.00000828 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 61.81 on 9 degrees of freedom
## Multiple R-squared: 0.9007, Adjusted R-squared: 0.8896
## F-statistic: 81.62 on 1 and 9 DF, p-value: 0.000008276
## [1] "SGT" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.296 -4.214 2.527 6.479 20.673
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4992.7955 7.6102 656.07 < 0.0000000000000002 ***
## op_count 8.0355 0.2573 31.23 0.000000000173 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.49 on 9 degrees of freedom
## Multiple R-squared: 0.9909, Adjusted R-squared: 0.9898
## F-statistic: 975.5 on 1 and 9 DF, p-value: 0.0000000001734
## [1] "EQ" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.409 -3.182 -2.073 8.318 18.536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4980.4091 7.3168 680.68 < 0.0000000000000002 ***
## op_count 7.0109 0.2474 28.34 0.000000000412 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.97 on 9 degrees of freedom
## Multiple R-squared: 0.9889, Adjusted R-squared: 0.9877
## F-statistic: 803.4 on 1 and 9 DF, p-value: 0.0000000004121
## [1] "ISZERO" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.591 -7.318 -5.909 -2.068 55.682
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4981.5909 11.5699 430.56 < 0.0000000000000002 ***
## op_count 4.5091 0.3911 11.53 0.00000108 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.51 on 9 degrees of freedom
## Multiple R-squared: 0.9366, Adjusted R-squared: 0.9295
## F-statistic: 132.9 on 1 and 9 DF, p-value: 0.000001083
## [1] "AND" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.477 -3.668 1.314 7.500 9.418
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4983.4773 5.9110 843.09 < 0.0000000000000002 ***
## op_count 7.0209 0.1998 35.13 0.0000000000606 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.48 on 9 degrees of freedom
## Multiple R-squared: 0.9928, Adjusted R-squared: 0.992
## F-statistic: 1234 on 1 and 9 DF, p-value: 0.00000000006058
## [1] "OR" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0455 -4.4773 0.6364 6.2955 12.8182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4974.0455 5.4688 909.54 < 0.0000000000000002 ***
## op_count 7.0091 0.1849 37.91 0.0000000000307 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.695 on 9 degrees of freedom
## Multiple R-squared: 0.9938, Adjusted R-squared: 0.9931
## F-statistic: 1437 on 1 and 9 DF, p-value: 0.00000000003068
## [1] "XOR" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.1591 -7.3841 -0.2364 7.3659 17.9818
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4980.1591 6.7769 734.88 < 0.0000000000000002 ***
## op_count 6.9718 0.2291 30.43 0.000000000219 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.01 on 9 degrees of freedom
## Multiple R-squared: 0.9904, Adjusted R-squared: 0.9893
## F-statistic: 926.1 on 1 and 9 DF, p-value: 0.0000000002187
## [1] "NOT" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.0455 -2.0455 -0.5455 1.2045 9.9545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4967.0454 2.8249 1758.29 < 0.0000000000000002 ***
## op_count 5.0000 0.0955 52.36 0.0000000000017 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.008 on 9 degrees of freedom
## Multiple R-squared: 0.9967, Adjusted R-squared: 0.9964
## F-statistic: 2741 on 1 and 9 DF, p-value: 0.0000000000017
## [1] "BYTE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.0455 -6.9545 -2.6818 0.9091 25.4091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4971.1364 7.2135 689.15 < 0.0000000000000002 ***
## op_count 8.2545 0.2439 33.85 0.0000000000845 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.79 on 9 degrees of freedom
## Multiple R-squared: 0.9922, Adjusted R-squared: 0.9913
## F-statistic: 1146 on 1 and 9 DF, p-value: 0.00000000008453
## [1] "SHL" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.205 -5.818 2.818 14.159 19.023
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5016.2045 11.2889 444.35 < 0.0000000000000002 ***
## op_count 7.9136 0.3816 20.74 0.0000000066 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.01 on 9 degrees of freedom
## Multiple R-squared: 0.9795, Adjusted R-squared: 0.9772
## F-statistic: 430 on 1 and 9 DF, p-value: 0.000000006599
## [1] "SHR" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.273 -3.782 4.255 5.768 9.018
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4981.7727 5.6987 874.20 < 0.0000000000000002 ***
## op_count 8.2473 0.1927 42.81 0.0000000000103 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.1 on 9 degrees of freedom
## Multiple R-squared: 0.9951, Adjusted R-squared: 0.9946
## F-statistic: 1833 on 1 and 9 DF, p-value: 0.00000000001034
## [1] "SAR" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.091 -3.118 2.336 4.832 11.327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4986.0909 6.1570 809.8 < 0.0000000000000002 ***
## op_count 8.6164 0.2081 41.4 0.000000000014 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.92 on 9 degrees of freedom
## Multiple R-squared: 0.9948, Adjusted R-squared: 0.9942
## F-statistic: 1714 on 1 and 9 DF, p-value: 0.00000000001396
## [1] "KECCAK256" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -324.14 -29.66 37.15 87.38 138.74
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4393.136 78.756 55.78 0.000000000000962 ***
## op_count 406.542 2.662 152.69 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 139.6 on 9 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9996
## F-statistic: 2.332e+04 on 1 and 9 DF, p-value: < 0.00000000000000022
## [1] "ADDRESS" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.6818 -2.1818 0.3182 4.0682 6.3182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3493.682 3.136 1114.1 <0.0000000000000002 ***
## op_count 20.200 0.106 190.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.559 on 9 degrees of freedom
## Multiple R-squared: 0.9998, Adjusted R-squared: 0.9997
## F-statistic: 3.631e+04 on 1 and 9 DF, p-value: < 0.00000000000000022
## [1] "ORIGIN" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.909 -5.209 -1.791 10.095 21.318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3515.9091 8.8986 395.11 < 0.0000000000000002 ***
## op_count 6.3109 0.3008 20.98 0.00000000596 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.78 on 9 degrees of freedom
## Multiple R-squared: 0.98, Adjusted R-squared: 0.9777
## F-statistic: 440.1 on 1 and 9 DF, p-value: 0.000000005956
## [1] "CALLER" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.664 -22.493 -4.823 -0.823 109.641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3476.2500 23.4478 148.25 < 0.0000000000000002 ***
## op_count 11.4536 0.7927 14.45 0.000000156 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 41.57 on 9 degrees of freedom
## Multiple R-squared: 0.9587, Adjusted R-squared: 0.9541
## F-statistic: 208.8 on 1 and 9 DF, p-value: 0.000000156
## [1] "CALLVALUE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.659 -2.450 2.696 5.552 11.555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3469.6591 5.8828 589.80 < 0.0000000000000002 ***
## op_count 4.9573 0.1989 24.93 0.00000000129 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.43 on 9 degrees of freedom
## Multiple R-squared: 0.9857, Adjusted R-squared: 0.9841
## F-statistic: 621.3 on 1 and 9 DF, p-value: 0.000000001292
## [1] "CALLDATALOAD" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -114.26 -46.52 16.75 34.47 122.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10951.795 43.665 250.81 < 0.0000000000000002 ***
## op_count 33.499 1.476 22.69 0.00000000297 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 77.41 on 9 degrees of freedom
## Multiple R-squared: 0.9828, Adjusted R-squared: 0.9809
## F-statistic: 515 on 1 and 9 DF, p-value: 0.000000002972
## [1] "CALLDATASIZE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.3864 -6.6909 0.5773 5.3273 11.3182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3464.3864 4.8057 720.89 < 0.0000000000000002 ***
## op_count 5.0518 0.1625 31.09 0.00000000018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.52 on 9 degrees of freedom
## Multiple R-squared: 0.9908, Adjusted R-squared: 0.9898
## F-statistic: 966.9 on 1 and 9 DF, p-value: 0.0000000001804
## [1] "CALLDATACOPY" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -107.07 -39.41 -13.21 49.34 89.09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10077.57 38.15 264.1 < 0.0000000000000002 ***
## op_count 39.73 1.29 30.8 0.000000000196 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 67.64 on 9 degrees of freedom
## Multiple R-squared: 0.9906, Adjusted R-squared: 0.9896
## F-statistic: 948.8 on 1 and 9 DF, p-value: 0.0000000001963
## [1] "CODESIZE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.682 -7.609 1.209 6.141 13.791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3469.6818 5.2766 657.56 < 0.0000000000000002 ***
## op_count 5.3055 0.1784 29.74 0.000000000268 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.354 on 9 degrees of freedom
## Multiple R-squared: 0.9899, Adjusted R-squared: 0.9888
## F-statistic: 884.6 on 1 and 9 DF, p-value: 0.0000000002683
## [1] "CODECOPY" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -157.25 -12.84 1.00 56.41 108.31
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10051.568 49.873 201.5 < 0.0000000000000002 ***
## op_count 26.637 1.686 15.8 0.0000000718 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 88.42 on 9 degrees of freedom
## Multiple R-squared: 0.9652, Adjusted R-squared: 0.9613
## F-statistic: 249.6 on 1 and 9 DF, p-value: 0.00000007182
## [1] "GASPRICE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.895 -9.152 -1.427 6.843 50.591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3471.9318 13.0653 265.7 < 0.0000000000000002 ***
## op_count 5.6991 0.4417 12.9 0.000000414 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.16 on 9 degrees of freedom
## Multiple R-squared: 0.9487, Adjusted R-squared: 0.943
## F-statistic: 166.5 on 1 and 9 DF, p-value: 0.0000004141
## [1] "EXTCODESIZE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -140.805 -38.109 -7.777 54.884 103.655
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11895.750 44.120 269.62 < 0.0000000000000002 ***
## op_count 54.403 1.492 36.47 0.0000000000434 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 78.22 on 9 degrees of freedom
## Multiple R-squared: 0.9933, Adjusted R-squared: 0.9925
## F-statistic: 1330 on 1 and 9 DF, p-value: 0.00000000004335
## [1] "EXTCODECOPY" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -198.95 -85.04 -4.62 59.05 352.55
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12218.614 91.264 133.88 0.000000000000000368 ***
## op_count 75.334 3.085 24.42 0.000000001552415127 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 161.8 on 9 degrees of freedom
## Multiple R-squared: 0.9851, Adjusted R-squared: 0.9835
## F-statistic: 596.2 on 1 and 9 DF, p-value: 0.000000001552
## [1] "RETURNDATASIZE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.236 -9.155 -0.518 4.009 48.655
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3478.9545 12.5158 277.96 < 0.0000000000000002 ***
## op_count 4.3891 0.4231 10.37 0.00000264 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.19 on 9 degrees of freedom
## Multiple R-squared: 0.9228, Adjusted R-squared: 0.9142
## F-statistic: 107.6 on 1 and 9 DF, p-value: 0.000002635
## [1] "RETURNDATACOPY" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -585.34 -26.03 8.58 90.47 344.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13476.591 143.700 93.783 0.00000000000000904 ***
## op_count 6.158 4.858 1.268 0.237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 254.8 on 9 degrees of freedom
## Multiple R-squared: 0.1515, Adjusted R-squared: 0.05722
## F-statistic: 1.607 on 1 and 9 DF, p-value: 0.2367
## [1] "EXTCODEHASH" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -114.92 -92.61 -27.86 85.59 156.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11935.000 59.548 200.43 < 0.0000000000000002 ***
## op_count 80.295 2.013 39.89 0.0000000000195 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 105.6 on 9 degrees of freedom
## Multiple R-squared: 0.9944, Adjusted R-squared: 0.9937
## F-statistic: 1591 on 1 and 9 DF, p-value: 0.00000000001947
## [1] "COINBASE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.364 -7.936 -3.609 3.809 29.973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3469.1818 7.4962 462.79 < 0.0000000000000002 ***
## op_count 7.5836 0.2534 29.93 0.000000000254 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.29 on 9 degrees of freedom
## Multiple R-squared: 0.9901, Adjusted R-squared: 0.9889
## F-statistic: 895.5 on 1 and 9 DF, p-value: 0.0000000002539
## [1] "TIMESTAMP" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.773 -16.214 -0.445 5.377 48.045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3473.7727 14.6174 237.646 < 0.0000000000000002 ***
## op_count 4.6673 0.4942 9.445 0.00000574 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.91 on 9 degrees of freedom
## Multiple R-squared: 0.9084, Adjusted R-squared: 0.8982
## F-statistic: 89.21 on 1 and 9 DF, p-value: 0.000005744
## [1] "NUMBER" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.645 -16.605 -3.636 6.814 53.191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3489.8182 14.4149 242.1 < 0.0000000000000002 ***
## op_count 5.2164 0.4873 10.7 0.00000202 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.55 on 9 degrees of freedom
## Multiple R-squared: 0.9272, Adjusted R-squared: 0.9191
## F-statistic: 114.6 on 1 and 9 DF, p-value: 0.000002025
## [1] "DIFFICULTY" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -45.664 -17.259 -0.355 15.800 48.991
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3462.0455 17.6627 196.01 < 0.0000000000000002 ***
## op_count 7.9655 0.5971 13.34 0.000000311 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.31 on 9 degrees of freedom
## Multiple R-squared: 0.9519, Adjusted R-squared: 0.9465
## F-statistic: 178 on 1 and 9 DF, p-value: 0.000000311
## [1] "GASLIMIT" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.06 -25.73 -10.89 18.11 77.83
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3453.5000 21.2646 162.406 < 0.0000000000000002 ***
## op_count 5.4891 0.7189 7.636 0.0000321 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37.7 on 9 degrees of freedom
## Multiple R-squared: 0.8663, Adjusted R-squared: 0.8514
## F-statistic: 58.3 on 1 and 9 DF, p-value: 0.00003206
## [1] "CHAINID" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.068 -28.291 -8.305 25.486 64.564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3450.0682 21.6164 159.60 < 0.0000000000000002 ***
## op_count 7.8736 0.7308 10.77 0.00000192 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38.32 on 9 degrees of freedom
## Multiple R-squared: 0.9281, Adjusted R-squared: 0.9201
## F-statistic: 116.1 on 1 and 9 DF, p-value: 0.000001917
## [1] "SELFBALANCE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -58.38 -21.63 -12.15 23.87 64.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3450.6591 24.0016 143.77 < 0.0000000000000002 ***
## op_count 33.9555 0.8114 41.85 0.0000000000127 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.55 on 9 degrees of freedom
## Multiple R-squared: 0.9949, Adjusted R-squared: 0.9943
## F-statistic: 1751 on 1 and 9 DF, p-value: 0.00000000001267
## [1] "POP" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.986 -32.736 -7.523 39.116 75.032
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4141.4318 27.4429 150.911 < 0.0000000000000002 ***
## op_count 6.3518 0.9277 6.847 0.000075 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48.65 on 9 degrees of freedom
## Multiple R-squared: 0.8389, Adjusted R-squared: 0.821
## F-statistic: 46.88 on 1 and 9 DF, p-value: 0.00007502
## [1] "MLOAD" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -249.98 -41.05 11.22 87.62 164.82
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10909.977 76.432 142.741 < 0.0000000000000002 ***
## op_count 13.090 2.584 5.066 0.000675 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 135.5 on 9 degrees of freedom
## Multiple R-squared: 0.7404, Adjusted R-squared: 0.7115
## F-statistic: 25.66 on 1 and 9 DF, p-value: 0.0006755
## [1] "MSTORE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.095 -23.168 -4.886 8.330 62.832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3352.8864 19.0456 176.04 < 0.0000000000000002 ***
## op_count 15.5427 0.6439 24.14 0.00000000172 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 33.76 on 9 degrees of freedom
## Multiple R-squared: 0.9848, Adjusted R-squared: 0.9831
## F-statistic: 582.7 on 1 and 9 DF, p-value: 0.000000001718
## [1] "MSTORE_COLD" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.84 -35.75 -10.66 22.83 83.17
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4192.8409 23.6485 177.3 < 0.0000000000000002 ***
## op_count 20.0664 0.7995 25.1 0.00000000122 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 41.92 on 9 degrees of freedom
## Multiple R-squared: 0.9859, Adjusted R-squared: 0.9844
## F-statistic: 630 on 1 and 9 DF, p-value: 0.000000001215
## [1] "MSTORE8" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -132.27 -33.65 4.00 34.32 109.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9903.159 37.373 264.98 < 0.0000000000000002 ***
## op_count 15.714 1.263 12.44 0.000000567 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 66.26 on 9 degrees of freedom
## Multiple R-squared: 0.945, Adjusted R-squared: 0.9389
## F-statistic: 154.7 on 1 and 9 DF, p-value: 0.0000005674
## [1] "JUMP" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -189.23 -23.22 21.16 53.09 77.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3735.727 46.168 80.916 0.0000000000000341 ***
## op_count 13.087 1.561 8.385 0.0000151738248240 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.85 on 9 degrees of freedom
## Multiple R-squared: 0.8865, Adjusted R-squared: 0.8739
## F-statistic: 70.31 on 1 and 9 DF, p-value: 0.00001517
## [1] "JUMPI" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -378.93 -45.40 43.40 73.18 176.74
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5428.932 90.729 59.84 0.000000000000512 ***
## op_count 16.255 3.067 5.30 0.000494 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 160.8 on 9 degrees of freedom
## Multiple R-squared: 0.7573, Adjusted R-squared: 0.7304
## F-statistic: 28.09 on 1 and 9 DF, p-value: 0.000494
## [1] "PC" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.64 -20.96 -11.34 17.62 48.69
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3466.5909 16.2917 212.782 < 0.0000000000000002 ***
## op_count 4.7055 0.5508 8.544 0.000013 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28.88 on 9 degrees of freedom
## Multiple R-squared: 0.8902, Adjusted R-squared: 0.878
## F-statistic: 72.99 on 1 and 9 DF, p-value: 0.00001304
## [1] "MSIZE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.86 -22.14 -14.43 19.24 53.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3487.341 16.950 205.741 < 0.0000000000000002 ***
## op_count 4.152 0.573 7.246 0.0000484 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.05 on 9 degrees of freedom
## Multiple R-squared: 0.8537, Adjusted R-squared: 0.8374
## F-statistic: 52.5 on 1 and 9 DF, p-value: 0.0000484
## [1] "GAS" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.60 -26.52 -11.99 27.89 59.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3458.7273 20.7778 166.463 < 0.0000000000000002 ***
## op_count 5.1218 0.7024 7.292 0.0000461 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 36.84 on 9 degrees of freedom
## Multiple R-squared: 0.8552, Adjusted R-squared: 0.8391
## F-statistic: 53.17 on 1 and 9 DF, p-value: 0.00004605
## [1] "JUMPDEST" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.973 -12.786 -1.482 11.250 35.536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2635.0455 12.4764 211.20 < 0.0000000000000002 ***
## op_count 4.3982 0.4218 10.43 0.00000252 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.12 on 9 degrees of freedom
## Multiple R-squared: 0.9236, Adjusted R-squared: 0.9151
## F-statistic: 108.7 on 1 and 9 DF, p-value: 0.000002522
## [1] "MCOPY" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -98.386 -27.307 -2.532 31.984 98.823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3802.386 33.204 114.5 0.0000000000000015 ***
## op_count 21.665 1.122 19.3 0.0000000124271686 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 58.86 on 9 degrees of freedom
## Multiple R-squared: 0.9764, Adjusted R-squared: 0.9738
## F-statistic: 372.5 on 1 and 9 DF, p-value: 0.00000001243
## [1] "MCOPY_COLD" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -92.545 -39.568 -4.464 46.405 78.218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4455.045 32.482 137.15 0.000000000000000296 ***
## op_count 26.647 1.098 24.27 0.000000001639893813 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 57.58 on 9 degrees of freedom
## Multiple R-squared: 0.9849, Adjusted R-squared: 0.9833
## F-statistic: 588.9 on 1 and 9 DF, p-value: 0.00000000164
## [1] "PUSH0" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.98 -18.73 -14.23 22.52 50.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3462.9773 19.4738 177.827 < 0.0000000000000002 ***
## op_count 3.8500 0.6583 5.848 0.000244 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34.52 on 9 degrees of freedom
## Multiple R-squared: 0.7917, Adjusted R-squared: 0.7685
## F-statistic: 34.2 on 1 and 9 DF, p-value: 0.0002443
## [1] "LOG0" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -752.7 -169.4 -47.2 212.7 753.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3798.795 245.483 15.47 0.000000086037 ***
## op_count 213.441 8.299 25.72 0.000000000978 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 435.2 on 9 degrees of freedom
## Multiple R-squared: 0.9866, Adjusted R-squared: 0.9851
## F-statistic: 661.5 on 1 and 9 DF, p-value: 0.0000000009784
## [1] "LOG1" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -460.64 -79.75 -12.18 87.09 519.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4245.455 162.591 26.11 0.0000000008553 ***
## op_count 235.255 5.497 42.80 0.0000000000104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 288.2 on 9 degrees of freedom
## Multiple R-squared: 0.9951, Adjusted R-squared: 0.9946
## F-statistic: 1832 on 1 and 9 DF, p-value: 0.00000000001036
## [1] "LOG2" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -386.5 -146.8 -27.5 102.9 503.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4636.09 158.26 29.30 0.00000000030705 ***
## op_count 254.38 5.35 47.55 0.00000000000403 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 280.6 on 9 degrees of freedom
## Multiple R-squared: 0.996, Adjusted R-squared: 0.9956
## F-statistic: 2261 on 1 and 9 DF, p-value: 0.000000000004034
## [1] "LOG3" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -278.73 -138.50 -8.68 128.59 363.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5045.955 116.297 43.39 0.000000000009163 ***
## op_count 267.573 3.932 68.06 0.000000000000161 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 206.2 on 9 degrees of freedom
## Multiple R-squared: 0.9981, Adjusted R-squared: 0.9978
## F-statistic: 4632 on 1 and 9 DF, p-value: 0.0000000000001613
## [1] "LOG4" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -365.91 -161.98 -5.86 122.73 350.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5528.182 138.058 40.04 0.000000000018804 ***
## op_count 278.936 4.667 59.77 0.000000000000518 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 244.8 on 9 degrees of freedom
## Multiple R-squared: 0.9975, Adjusted R-squared: 0.9972
## F-statistic: 3572 on 1 and 9 DF, p-value: 0.0000000000005181
## [1] "CREATE" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2725.6 -763.8 120.1 958.1 2321.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5238.82 858.46 6.103 0.000179 ***
## op_count 7374.92 29.02 254.121 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1522 on 9 degrees of freedom
## Multiple R-squared: 0.9999, Adjusted R-squared: 0.9998
## F-statistic: 6.458e+04 on 1 and 9 DF, p-value: < 0.00000000000000022
## [1] "CALL" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -374.9 -258.7 -16.9 137.6 728.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16929.386 207.450 81.61 0.0000000000000315 ***
## op_count 492.701 7.013 70.25 0.0000000000001212 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 367.8 on 9 degrees of freedom
## Multiple R-squared: 0.9982, Adjusted R-squared: 0.998
## F-statistic: 4936 on 1 and 9 DF, p-value: 0.0000000000001212
## [1] "RETURN" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -800.2 -317.3 152.5 288.2 593.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42595.341 261.383 162.962 <0.0000000000000002 ***
## op_count 24.008 8.836 2.717 0.0237 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 463.4 on 9 degrees of freedom
## Multiple R-squared: 0.4506, Adjusted R-squared: 0.3896
## F-statistic: 7.382 on 1 and 9 DF, p-value: 0.02372
## [1] "DELEGATECALL" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -524.73 -117.38 -2.01 177.40 449.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15644.727 158.889 98.46 0.00000000000000583 ***
## op_count 384.895 5.371 71.66 0.00000000000010150 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 281.7 on 9 degrees of freedom
## Multiple R-squared: 0.9983, Adjusted R-squared: 0.9981
## F-statistic: 5135 on 1 and 9 DF, p-value: 0.0000000000001015
## [1] "STATICCALL" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -527.52 -239.95 43.71 217.50 552.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15827.023 198.251 79.83 0.0000000000000384 ***
## op_count 436.326 6.702 65.10 0.0000000000002403 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 351.5 on 9 degrees of freedom
## Multiple R-squared: 0.9979, Adjusted R-squared: 0.9976
## F-statistic: 4238 on 1 and 9 DF, p-value: 0.0000000000002403
## [1] "REVERT" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -318.09 -106.19 38.11 112.01 305.31
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42797.591 108.001 396.27 < 0.0000000000000002 ***
## op_count 63.060 3.651 17.27 0.000000033 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 191.5 on 9 degrees of freedom
## Multiple R-squared: 0.9707, Adjusted R-squared: 0.9675
## F-statistic: 298.3 on 1 and 9 DF, p-value: 0.00000003297
## [1] "PUSH1" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.8864 -3.8114 -0.2955 4.2477 11.6727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3420.3864 4.5923 744.81 < 0.0000000000000002 ***
## op_count 5.9882 0.1552 38.57 0.0000000000263 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.141 on 9 degrees of freedom
## Multiple R-squared: 0.994, Adjusted R-squared: 0.9933
## F-statistic: 1488 on 1 and 9 DF, p-value: 0.00000000002629
## [1] "PUSH2" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.586 -15.220 0.623 13.582 39.832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3413.7500 13.3610 255.50 < 0.0000000000000002 ***
## op_count 9.9209 0.4517 21.96 0.00000000397 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.69 on 9 degrees of freedom
## Multiple R-squared: 0.9817, Adjusted R-squared: 0.9797
## F-statistic: 482.4 on 1 and 9 DF, p-value: 0.000000003968
## [1] "PUSH3" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.773 -16.836 3.455 26.555 39.300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3394.7727 18.5501 183.00 < 0.0000000000000002 ***
## op_count 9.9855 0.6271 15.92 0.0000000671 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.89 on 9 degrees of freedom
## Multiple R-squared: 0.9657, Adjusted R-squared: 0.9619
## F-statistic: 253.5 on 1 and 9 DF, p-value: 0.00000006709
## [1] "PUSH4" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.886 -13.832 1.668 16.557 35.132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3402.3864 15.0227 226.48 < 0.0000000000000002 ***
## op_count 9.9482 0.5079 19.59 0.0000000109 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.63 on 9 degrees of freedom
## Multiple R-squared: 0.9771, Adjusted R-squared: 0.9745
## F-statistic: 383.7 on 1 and 9 DF, p-value: 0.00000001091
## [1] "PUSH5" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.386 -3.293 2.959 9.227 11.727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3414.8864 6.9494 491.4 < 0.0000000000000002 ***
## op_count 9.1155 0.2349 38.8 0.0000000000249 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.32 on 9 degrees of freedom
## Multiple R-squared: 0.9941, Adjusted R-squared: 0.9934
## F-statistic: 1505 on 1 and 9 DF, p-value: 0.00000000002493
## [1] "PUSH6" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.7955 -7.8477 0.8455 7.7136 16.3364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3406.796 6.507 523.53 < 0.0000000000000002 ***
## op_count 10.024 0.220 45.57 0.0000000000059 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.54 on 9 degrees of freedom
## Multiple R-squared: 0.9957, Adjusted R-squared: 0.9952
## F-statistic: 2077 on 1 and 9 DF, p-value: 0.000000000005904
## [1] "PUSH7" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.0682 -3.5432 0.7636 5.8182 13.2091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3416.5682 5.5497 615.63 < 0.0000000000000002 ***
## op_count 10.1445 0.1876 54.07 0.00000000000127 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.839 on 9 degrees of freedom
## Multiple R-squared: 0.9969, Adjusted R-squared: 0.9966
## F-statistic: 2924 on 1 and 9 DF, p-value: 0.000000000001273
## [1] "PUSH8" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.827 -13.132 -9.523 5.868 81.709
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3409.7045 20.5182 166.18 < 0.0000000000000002 ***
## op_count 10.6464 0.6936 15.35 0.0000000924 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 36.37 on 9 degrees of freedom
## Multiple R-squared: 0.9632, Adjusted R-squared: 0.9591
## F-statistic: 235.6 on 1 and 9 DF, p-value: 0.00000009239
## [1] "PUSH9" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.95 -18.00 -6.00 12.78 60.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3426.5455 16.8432 203.44 < 0.0000000000000002 ***
## op_count 9.0982 0.5694 15.98 0.0000000651 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.86 on 9 degrees of freedom
## Multiple R-squared: 0.9659, Adjusted R-squared: 0.9622
## F-statistic: 255.3 on 1 and 9 DF, p-value: 0.00000006509
## [1] "PUSH10" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -71.182 -49.682 1.136 48.682 71.500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3342.89 31.66 105.60 0.00000000000000311 ***
## op_count 14.73 1.07 13.77 0.00000023730736255 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56.12 on 9 degrees of freedom
## Multiple R-squared: 0.9547, Adjusted R-squared: 0.9496
## F-statistic: 189.5 on 1 and 9 DF, p-value: 0.0000002373
## [1] "PUSH11" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.864 -4.295 -2.673 3.986 18.200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3423.3636 4.8425 706.95 < 0.0000000000000002 ***
## op_count 9.4873 0.1637 57.95 0.000000000000683 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.585 on 9 degrees of freedom
## Multiple R-squared: 0.9973, Adjusted R-squared: 0.997
## F-statistic: 3359 on 1 and 9 DF, p-value: 0.000000000000683
## [1] "PUSH12" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.1136 -7.8636 -0.8636 6.6864 16.4364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3423.6136 6.3931 535.52 < 0.0000000000000002 ***
## op_count 8.7700 0.2161 40.58 0.0000000000167 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.33 on 9 degrees of freedom
## Multiple R-squared: 0.9946, Adjusted R-squared: 0.994
## F-statistic: 1647 on 1 and 9 DF, p-value: 0.00000000001669
## [1] "PUSH13" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.636 -6.509 5.191 8.673 11.500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3406.136 7.128 477.84 < 0.0000000000000002 ***
## op_count 10.134 0.241 42.06 0.0000000000121 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.64 on 9 degrees of freedom
## Multiple R-squared: 0.9949, Adjusted R-squared: 0.9944
## F-statistic: 1769 on 1 and 9 DF, p-value: 0.00000000001212
## [1] "PUSH14" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.477 -5.584 4.741 8.545 14.132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3417.4773 7.9395 430.44 < 0.0000000000000002 ***
## op_count 9.9391 0.2684 37.03 0.0000000000379 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.08 on 9 degrees of freedom
## Multiple R-squared: 0.9935, Adjusted R-squared: 0.9928
## F-statistic: 1371 on 1 and 9 DF, p-value: 0.00000000003787
## [1] "PUSH15" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.864 -2.954 2.773 8.836 23.400
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3414.8636 9.6391 354.27 < 0.0000000000000002 ***
## op_count 10.4473 0.3259 32.06 0.000000000137 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.09 on 9 degrees of freedom
## Multiple R-squared: 0.9913, Adjusted R-squared: 0.9904
## F-statistic: 1028 on 1 and 9 DF, p-value: 0.0000000001373
## [1] "PUSH16" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.773 -13.318 -0.818 21.341 32.273
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3415.2727 14.8597 229.84 < 0.0000000000000002 ***
## op_count 8.8182 0.5023 17.55 0.0000000286 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.34 on 9 degrees of freedom
## Multiple R-squared: 0.9716, Adjusted R-squared: 0.9685
## F-statistic: 308.1 on 1 and 9 DF, p-value: 0.0000000286
## [1] "PUSH17" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.200 -29.832 5.727 14.659 62.809
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3401.5455 19.6307 173.28 < 0.0000000000000002 ***
## op_count 10.0145 0.6636 15.09 0.000000107 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34.8 on 9 degrees of freedom
## Multiple R-squared: 0.962, Adjusted R-squared: 0.9578
## F-statistic: 227.7 on 1 and 9 DF, p-value: 0.0000001071
## [1] "PUSH18" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.600 -18.541 -5.982 9.405 57.345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3400.5455 15.8826 214.10 < 0.0000000000000002 ***
## op_count 10.1109 0.5369 18.83 0.0000000154 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 28.16 on 9 degrees of freedom
## Multiple R-squared: 0.9752, Adjusted R-squared: 0.9725
## F-statistic: 354.6 on 1 and 9 DF, p-value: 0.00000001543
## [1] "PUSH19" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.864 -5.759 2.200 7.423 12.454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3401.8636 6.2877 541.03 < 0.0000000000000002 ***
## op_count 10.7873 0.2126 50.75 0.00000000000225 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.15 on 9 degrees of freedom
## Multiple R-squared: 0.9965, Adjusted R-squared: 0.9961
## F-statistic: 2575 on 1 and 9 DF, p-value: 0.000000000002248
## [1] "PUSH20" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.0227 -3.5682 0.4091 8.5455 10.5909
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3402.0227 5.8087 585.67 < 0.0000000000000002 ***
## op_count 9.9682 0.1964 50.76 0.00000000000224 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.3 on 9 degrees of freedom
## Multiple R-squared: 0.9965, Adjusted R-squared: 0.9961
## F-statistic: 2577 on 1 and 9 DF, p-value: 0.000000000002243
## [1] "PUSH21" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.682 -1.750 2.273 4.273 32.282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3401.7727 9.3412 364.17 < 0.0000000000000002 ***
## op_count 10.1982 0.3158 32.29 0.000000000129 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.56 on 9 degrees of freedom
## Multiple R-squared: 0.9914, Adjusted R-squared: 0.9905
## F-statistic: 1043 on 1 and 9 DF, p-value: 0.0000000001287
## [1] "PUSH22" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.114 -3.966 0.955 12.239 16.227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3412.6136 8.9141 382.83 < 0.0000000000000002 ***
## op_count 11.5318 0.3014 38.27 0.0000000000282 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.8 on 9 degrees of freedom
## Multiple R-squared: 0.9939, Adjusted R-squared: 0.9932
## F-statistic: 1464 on 1 and 9 DF, p-value: 0.00000000002822
## [1] "PUSH23" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -33.614 -11.336 2.905 9.659 31.391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3402.1136 10.3301 329.3 < 0.0000000000000002 ***
## op_count 11.6991 0.3492 33.5 0.0000000000927 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.31 on 9 degrees of freedom
## Multiple R-squared: 0.992, Adjusted R-squared: 0.9912
## F-statistic: 1122 on 1 and 9 DF, p-value: 0.00000000009273
## [1] "PUSH24" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.673 -16.318 -7.964 10.182 54.918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3396.9091 14.4138 235.7 < 0.0000000000000002 ***
## op_count 10.5764 0.4873 21.7 0.00000000441 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.55 on 9 degrees of freedom
## Multiple R-squared: 0.9813, Adjusted R-squared: 0.9792
## F-statistic: 471.1 on 1 and 9 DF, p-value: 0.000000004408
## [1] "PUSH25" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.341 -8.829 2.136 9.693 16.454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3405.3409 8.1720 416.71 < 0.0000000000000002 ***
## op_count 9.9682 0.2763 36.08 0.0000000000478 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.49 on 9 degrees of freedom
## Multiple R-squared: 0.9931, Adjusted R-squared: 0.9924
## F-statistic: 1302 on 1 and 9 DF, p-value: 0.00000000004775
## [1] "PUSH26" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.227 -6.627 2.273 7.623 13.473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3405.7273 5.8296 584.21 < 0.0000000000000002 ***
## op_count 10.3600 0.1971 52.57 0.00000000000164 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.33 on 9 degrees of freedom
## Multiple R-squared: 0.9968, Adjusted R-squared: 0.9964
## F-statistic: 2763 on 1 and 9 DF, p-value: 0.000000000001639
## [1] "PUSH27" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.296 -3.280 3.186 6.566 12.600
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3413.2955 6.5522 520.93 < 0.0000000000000002 ***
## op_count 9.9173 0.2215 44.77 0.00000000000692 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.62 on 9 degrees of freedom
## Multiple R-squared: 0.9955, Adjusted R-squared: 0.995
## F-statistic: 2005 on 1 and 9 DF, p-value: 0.000000000006916
## [1] "PUSH28" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.636 -6.295 2.546 12.750 16.591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3412.3636 9.5525 357.22 < 0.0000000000000002 ***
## op_count 10.2727 0.3229 31.81 0.000000000147 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.93 on 9 degrees of freedom
## Multiple R-squared: 0.9912, Adjusted R-squared: 0.9902
## F-statistic: 1012 on 1 and 9 DF, p-value: 0.0000000001472
## [1] "PUSH29" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.882 -9.791 -2.727 3.636 26.691
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3417.7727 8.3980 406.98 < 0.0000000000000002 ***
## op_count 10.3691 0.2839 36.52 0.0000000000428 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.89 on 9 degrees of freedom
## Multiple R-squared: 0.9933, Adjusted R-squared: 0.9926
## F-statistic: 1334 on 1 and 9 DF, p-value: 0.00000000004283
## [1] "PUSH30" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.409 -17.277 3.055 11.982 67.518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3409.091 18.458 184.70 < 0.0000000000000002 ***
## op_count 10.496 0.624 16.82 0.0000000415 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.72 on 9 degrees of freedom
## Multiple R-squared: 0.9692, Adjusted R-squared: 0.9657
## F-statistic: 283 on 1 and 9 DF, p-value: 0.00000004154
## [1] "PUSH31" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.000 -5.409 -1.446 6.859 14.609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3411.5000 6.1343 556.14 < 0.0000000000000002 ***
## op_count 11.1964 0.2074 53.99 0.00000000000129 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.87 on 9 degrees of freedom
## Multiple R-squared: 0.9969, Adjusted R-squared: 0.9966
## F-statistic: 2915 on 1 and 9 DF, p-value: 0.00000000000129
## [1] "PUSH32" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.250 -5.705 2.546 8.648 11.264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3405.2500 5.4755 621.90 < 0.0000000000000002 ***
## op_count 10.7282 0.1851 57.96 0.000000000000683 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.707 on 9 degrees of freedom
## Multiple R-squared: 0.9973, Adjusted R-squared: 0.997
## F-statistic: 3359 on 1 and 9 DF, p-value: 0.0000000000006826
## [1] "DUP1" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.159 -5.973 -2.732 5.425 12.546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4888.6591 4.5159 1082.54 < 0.0000000000000002 ***
## op_count 5.7518 0.1527 37.68 0.0000000000324 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.006 on 9 degrees of freedom
## Multiple R-squared: 0.9937, Adjusted R-squared: 0.993
## F-statistic: 1419 on 1 and 9 DF, p-value: 0.00000000003244
## [1] "DUP2" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.023 -3.375 1.182 2.511 7.068
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4887.02273 2.91501 1676.50 < 0.0000000000000002 ***
## op_count 5.59545 0.09855 56.78 0.000000000000821 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.168 on 9 degrees of freedom
## Multiple R-squared: 0.9972, Adjusted R-squared: 0.9969
## F-statistic: 3224 on 1 and 9 DF, p-value: 0.0000000000008206
## [1] "DUP3" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.14 -14.03 -10.10 11.76 55.44
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4905.9545 15.1576 323.66 < 0.0000000000000002 ***
## op_count 5.3036 0.5124 10.35 0.00000268 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.87 on 9 degrees of freedom
## Multiple R-squared: 0.9225, Adjusted R-squared: 0.9139
## F-statistic: 107.1 on 1 and 9 DF, p-value: 0.000002685
## [1] "DUP4" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.427 -22.836 -3.109 22.268 44.109
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4890.182 16.860 290.049 < 0.0000000000000002 ***
## op_count 5.193 0.570 9.111 0.00000773 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 29.89 on 9 degrees of freedom
## Multiple R-squared: 0.9022, Adjusted R-squared: 0.8913
## F-statistic: 83 on 1 and 9 DF, p-value: 0.000007726
## [1] "DUP5" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -47.064 -20.823 6.955 16.786 51.418
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4920.1364 18.3295 268.427 < 0.0000000000000002 ***
## op_count 4.5964 0.6197 7.418 0.0000403 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 32.49 on 9 degrees of freedom
## Multiple R-squared: 0.8594, Adjusted R-squared: 0.8438
## F-statistic: 55.02 on 1 and 9 DF, p-value: 0.00004027
## [1] "DUP6" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.159 -3.066 1.014 6.434 11.677
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4878.6591 6.1188 797.32 < 0.0000000000000002 ***
## op_count 5.7664 0.2069 27.88 0.000000000478 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.85 on 9 degrees of freedom
## Multiple R-squared: 0.9886, Adjusted R-squared: 0.9873
## F-statistic: 777.1 on 1 and 9 DF, p-value: 0.0000000004778
## [1] "DUP7" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.546 -7.727 1.609 4.773 20.618
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4892.0455 6.4073 763.51 < 0.0000000000000002 ***
## op_count 5.8673 0.2166 27.09 0.000000000617 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.36 on 9 degrees of freedom
## Multiple R-squared: 0.9879, Adjusted R-squared: 0.9865
## F-statistic: 733.7 on 1 and 9 DF, p-value: 0.0000000006171
## [1] "DUP8" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.954 -3.841 2.082 3.954 14.827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4906.9545 5.1664 949.8 < 0.0000000000000002 ***
## op_count 6.0436 0.1747 34.6 0.0000000000694 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.159 on 9 degrees of freedom
## Multiple R-squared: 0.9925, Adjusted R-squared: 0.9917
## F-statistic: 1197 on 1 and 9 DF, p-value: 0.00000000006943
## [1] "DUP9" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.091 -13.591 1.636 12.591 19.454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4870.2727 9.5169 511.75 < 0.0000000000000002 ***
## op_count 6.1545 0.3217 19.13 0.0000000134 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.87 on 9 degrees of freedom
## Multiple R-squared: 0.976, Adjusted R-squared: 0.9733
## F-statistic: 365.9 on 1 and 9 DF, p-value: 0.00000001344
## [1] "DUP10" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.500 -11.027 1.409 6.955 29.582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4874.5000 8.9691 543.5 < 0.0000000000000002 ***
## op_count 5.9418 0.3032 19.6 0.0000000109 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.9 on 9 degrees of freedom
## Multiple R-squared: 0.9771, Adjusted R-squared: 0.9746
## F-statistic: 384 on 1 and 9 DF, p-value: 0.00000001087
## [1] "DUP11" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -61.09 -24.10 11.77 22.55 53.05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4917.3864 20.3452 241.698 < 0.0000000000000002 ***
## op_count 6.4282 0.6878 9.346 0.00000626 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 36.07 on 9 degrees of freedom
## Multiple R-squared: 0.9066, Adjusted R-squared: 0.8962
## F-statistic: 87.35 on 1 and 9 DF, p-value: 0.000006264
## [1] "DUP12" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.48 -15.78 16.66 17.91 35.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4864.7045 17.4851 278.22 < 0.0000000000000002 ***
## op_count 5.9755 0.5911 10.11 0.00000327 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31 on 9 degrees of freedom
## Multiple R-squared: 0.9191, Adjusted R-squared: 0.9101
## F-statistic: 102.2 on 1 and 9 DF, p-value: 0.000003269
## [1] "DUP13" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.0682 -2.3864 -0.8864 1.1932 9.5455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4899.0682 3.0352 1614.09 < 0.0000000000000002 ***
## op_count 5.4773 0.1026 53.38 0.00000000000143 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.381 on 9 degrees of freedom
## Multiple R-squared: 0.9969, Adjusted R-squared: 0.9965
## F-statistic: 2849 on 1 and 9 DF, p-value: 0.000000000001428
## [1] "DUP14" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.500 -3.482 3.773 4.718 12.218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4902.000 5.531 886.26 < 0.0000000000000002 ***
## op_count 5.689 0.187 30.43 0.000000000219 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.806 on 9 degrees of freedom
## Multiple R-squared: 0.9904, Adjusted R-squared: 0.9893
## F-statistic: 925.7 on 1 and 9 DF, p-value: 0.0000000002191
## [1] "DUP15" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.454 -3.755 2.546 7.146 9.445
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4877.9545 5.2416 930.63 < 0.0000000000000002 ***
## op_count 5.7200 0.1772 32.28 0.000000000129 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.292 on 9 degrees of freedom
## Multiple R-squared: 0.9914, Adjusted R-squared: 0.9905
## F-statistic: 1042 on 1 and 9 DF, p-value: 0.0000000001292
## [1] "DUP16" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -60.250 -13.364 -7.795 6.966 125.545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4968.7955 28.0914 176.879 < 0.0000000000000002 ***
## op_count 6.5591 0.9497 6.907 0.0000701 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49.8 on 9 degrees of freedom
## Multiple R-squared: 0.8413, Adjusted R-squared: 0.8236
## F-statistic: 47.7 on 1 and 9 DF, p-value: 0.00007014
## [1] "SWAP1" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.859 -18.784 7.905 18.339 26.518
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4062.1136 14.9448 271.81 < 0.0000000000000002 ***
## op_count 6.4245 0.5052 12.72 0.000000469 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.49 on 9 degrees of freedom
## Multiple R-squared: 0.9473, Adjusted R-squared: 0.9414
## F-statistic: 161.7 on 1 and 9 DF, p-value: 0.0000004692
## [1] "SWAP2" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.704 -3.266 -1.395 4.700 9.209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4079.7045 3.8106 1070.62 < 0.0000000000000002 ***
## op_count 6.2173 0.1288 48.26 0.00000000000353 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.755 on 9 degrees of freedom
## Multiple R-squared: 0.9962, Adjusted R-squared: 0.9957
## F-statistic: 2329 on 1 and 9 DF, p-value: 0.000000000003528
## [1] "SWAP3" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.682 -3.818 -2.546 3.273 15.182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4079.0455 4.1621 980.04 < 0.0000000000000002 ***
## op_count 6.1818 0.1407 43.94 0.00000000000819 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.379 on 9 degrees of freedom
## Multiple R-squared: 0.9954, Adjusted R-squared: 0.9948
## F-statistic: 1930 on 1 and 9 DF, p-value: 0.000000000008191
## [1] "SWAP4" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.155 -12.682 1.791 10.145 66.136
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4058.0000 17.1025 237.28 < 0.0000000000000002 ***
## op_count 7.3473 0.5782 12.71 0.000000472 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.32 on 9 degrees of freedom
## Multiple R-squared: 0.9472, Adjusted R-squared: 0.9413
## F-statistic: 161.5 on 1 and 9 DF, p-value: 0.0000004719
## [1] "SWAP5" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.2045 -4.0455 -0.7136 2.7864 16.2909
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4089.2045 4.6310 883.01 < 0.0000000000000002 ***
## op_count 5.8336 0.1566 37.26 0.0000000000358 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.21 on 9 degrees of freedom
## Multiple R-squared: 0.9936, Adjusted R-squared: 0.9928
## F-statistic: 1388 on 1 and 9 DF, p-value: 0.00000000003581
## [1] "SWAP6" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.677 -13.520 0.532 9.136 40.741
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4100.8409 11.0261 371.92 < 0.0000000000000002 ***
## op_count 5.8209 0.3728 15.62 0.0000000795 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.55 on 9 degrees of freedom
## Multiple R-squared: 0.9644, Adjusted R-squared: 0.9605
## F-statistic: 243.9 on 1 and 9 DF, p-value: 0.00000007949
## [1] "SWAP7" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -86.273 -20.218 4.409 20.814 78.600
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4145.2727 27.3779 151.410 < 0.0000000000000002 ***
## op_count 6.1127 0.9255 6.604 0.0000988 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48.54 on 9 degrees of freedom
## Multiple R-squared: 0.829, Adjusted R-squared: 0.81
## F-statistic: 43.62 on 1 and 9 DF, p-value: 0.00009876
## [1] "SWAP8" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.73 -24.80 11.96 22.89 32.82
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4137.3636 17.1639 241.05 < 0.0000000000000002 ***
## op_count 5.8818 0.5802 10.14 0.0000032 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.43 on 9 degrees of freedom
## Multiple R-squared: 0.9195, Adjusted R-squared: 0.9105
## F-statistic: 102.8 on 1 and 9 DF, p-value: 0.000003195
## [1] "SWAP9" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -95.25 -67.95 -48.49 33.51 256.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4011.341 61.409 65.322 0.000000000000233 ***
## op_count 13.954 2.076 6.721 0.000086409600375 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 108.9 on 9 degrees of freedom
## Multiple R-squared: 0.8339, Adjusted R-squared: 0.8154
## F-statistic: 45.18 on 1 and 9 DF, p-value: 0.00008641
## [1] "SWAP10" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -61.155 -16.666 0.927 21.805 57.055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4234.3409 22.1793 190.914 < 0.0000000000000002 ***
## op_count 4.1209 0.7498 5.496 0.000382 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39.32 on 9 degrees of freedom
## Multiple R-squared: 0.7704, Adjusted R-squared: 0.7449
## F-statistic: 30.21 on 1 and 9 DF, p-value: 0.0003821
## [1] "SWAP11" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.39 -31.48 -19.57 15.46 138.77
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4128.386 31.850 129.621 0.000000000000000492 ***
## op_count 5.114 1.077 4.749 0.00105 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56.46 on 9 degrees of freedom
## Multiple R-squared: 0.7148, Adjusted R-squared: 0.6831
## F-statistic: 22.56 on 1 and 9 DF, p-value: 0.001045
## [1] "SWAP12" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.582 -6.955 -3.209 7.791 27.546
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4093.7727 9.1570 447.06 < 0.0000000000000002 ***
## op_count 6.4873 0.3096 20.96 0.00000000601 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.23 on 9 degrees of freedom
## Multiple R-squared: 0.9799, Adjusted R-squared: 0.9777
## F-statistic: 439.2 on 1 and 9 DF, p-value: 0.000000006012
## [1] "SWAP13" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.036 -16.252 -7.282 19.086 88.591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4068.9773 24.4206 166.621 < 0.0000000000000002 ***
## op_count 7.2373 0.8256 8.766 0.0000106 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.29 on 9 degrees of freedom
## Multiple R-squared: 0.8952, Adjusted R-squared: 0.8835
## F-statistic: 76.85 on 1 and 9 DF, p-value: 0.00001058
## [1] "SWAP14" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.136 -16.193 -4.568 14.807 55.227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4094.477 15.500 264.15 < 0.0000000000000002 ***
## op_count 5.832 0.524 11.13 0.00000146 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 27.48 on 9 degrees of freedom
## Multiple R-squared: 0.9323, Adjusted R-squared: 0.9247
## F-statistic: 123.9 on 1 and 9 DF, p-value: 0.000001459
## [1] "SWAP15" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.8909 -4.2273 -0.8636 2.8591 11.8182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4170.1818 3.5384 1178.55 < 0.0000000000000002 ***
## op_count 6.0236 0.1196 50.36 0.00000000000241 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.273 on 9 degrees of freedom
## Multiple R-squared: 0.9965, Adjusted R-squared: 0.9961
## F-statistic: 2536 on 1 and 9 DF, p-value: 0.00000000000241
## [1] "SWAP16" "geth"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.3864 -1.5795 -0.4773 1.1705 10.3182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4124.2045 3.6169 1140.26 < 0.0000000000000002 ***
## op_count 6.0591 0.1223 49.55 0.00000000000278 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.412 on 9 degrees of freedom
## Multiple R-squared: 0.9963, Adjusted R-squared: 0.9959
## F-statistic: 2456 on 1 and 9 DF, p-value: 0.000000000002784
The resulting estimates based on the regressions above.
estimates## op estimate_marginal_ns estimate_marginal_ns_stderr env
## 1 ADD -51.440909 4.00606261 geth
## 2 MUL 13.004545 0.99019690 geth
## 3 SUB 5.244545 2.00360875 geth
## 4 DIV 7.806364 0.18376508 geth
## 5 SDIV 9.580000 0.15556290 geth
## 6 MOD 7.410909 0.24734699 geth
## 7 SMOD 9.315455 0.14150282 geth
## 8 ADDMOD 12.614545 0.26053276 geth
## 9 MULMOD 22.426364 0.27870536 geth
## 10 EXP 26.974545 0.38723384 geth
## 11 SIGNEXTEND 9.003636 0.19296726 geth
## 12 LT 7.322727 0.18791666 geth
## 13 GT 7.390000 0.20104845 geth
## 14 SLT 10.649091 1.17873584 geth
## 15 SGT 8.035455 0.25727237 geth
## 16 EQ 7.010909 0.24735441 geth
## 17 ISZERO 4.509091 0.39113454 geth
## 18 AND 7.020909 0.19982798 geth
## 19 OR 7.009091 0.18487829 geth
## 20 XOR 6.971818 0.22909953 geth
## 21 NOT 5.000000 0.09550023 geth
## 22 BYTE 8.254545 0.24385939 geth
## 23 SHL 7.913636 0.38163476 geth
## 24 SHR 8.247273 0.19265102 geth
## 25 SAR 8.616364 0.20814366 geth
## 26 KECCAK256 406.541818 2.66244611 geth
## 27 ADDRESS 20.200000 0.10600865 geth
## 28 ORIGIN 6.310909 0.30082775 geth
## 29 CALLER 11.453636 0.79268111 geth
## 30 CALLVALUE 4.957273 0.19887447 geth
## 31 CALLDATALOAD 33.499091 1.47615635 geth
## 32 CALLDATASIZE 5.051818 0.16246240 geth
## 33 CALLDATACOPY 39.728182 1.28979675 geth
## 34 CODESIZE 5.305455 0.17838269 geth
## 35 CODECOPY 26.637273 1.68601712 geth
## 36 GASPRICE 5.699091 0.44168670 geth
## 37 EXTCODESIZE 54.402727 1.49153322 geth
## 38 EXTCODECOPY 75.333636 3.08528679 geth
## 39 RETURNDATASIZE 4.389091 0.42311061 geth
## 40 RETURNDATACOPY 6.158182 4.85793288 geth
## 41 EXTCODEHASH 80.294545 2.01307307 geth
## 42 COINBASE 7.583636 0.25341669 geth
## 43 TIMESTAMP 4.667273 0.49415909 geth
## 44 NUMBER 5.216364 0.48731155 geth
## 45 DIFFICULTY 7.965455 0.59710935 geth
## 46 GASLIMIT 5.489091 0.71887551 geth
## 47 CHAINID 7.873636 0.73076680 geth
## 48 SELFBALANCE 33.955455 0.81140284 geth
## 49 POP 6.351818 0.92773859 geth
## 50 MLOAD 13.090000 2.58387312 geth
## 51 MSTORE 15.542727 0.64385853 geth
## 52 MSTORE_COLD 20.066364 0.79946671 geth
## 53 MSTORE8 15.713636 1.26344868 geth
## 54 JUMP 13.087273 1.56076857 geth
## 55 JUMPI 16.255455 3.06719811 geth
## 56 PC 4.705455 0.55076072 geth
## 57 MSIZE 4.151818 0.57301828 geth
## 58 GAS 5.121818 0.70241640 geth
## 59 JUMPDEST 4.398182 0.42178030 geth
## 60 MCOPY 21.664545 1.12249799 geth
## 61 MCOPY_COLD 26.647273 1.09809820 geth
## 62 PUSH0 3.850000 0.65833604 geth
## 63 LOG0 213.440909 8.29884021 geth
## 64 LOG1 235.254545 5.49659382 geth
## 65 LOG2 254.376364 5.35003054 geth
## 66 LOG3 267.572727 3.93156682 geth
## 67 LOG4 278.936364 4.66722409 geth
## 68 CREATE 7374.918182 29.02125572 geth
## 69 CALL 492.700909 7.01310003 geth
## 70 RETURN 24.008182 8.83634721 geth
## 71 DELEGATECALL 384.894545 5.37142205 geth
## 72 STATICCALL 436.326364 6.70211703 geth
## 73 REVERT 63.060000 3.65110753 geth
## 74 PUSH1 5.988182 0.15524825 geth
## 75 PUSH2 9.920909 0.45168504 geth
## 76 PUSH3 9.985455 0.62710904 geth
## 77 PUSH4 9.948182 0.50785845 geth
## 78 PUSH5 9.115455 0.23493175 geth
## 79 PUSH6 10.024545 0.21998643 geth
## 80 PUSH7 10.144545 0.18761443 geth
## 81 PUSH8 10.646364 0.69364020 geth
## 82 PUSH9 9.098182 0.56940483 geth
## 83 PUSH10 14.731818 1.07020912 geth
## 84 PUSH11 9.487273 0.16370481 geth
## 85 PUSH12 8.770000 0.21612477 geth
## 86 PUSH13 10.134545 0.24097598 geth
## 87 PUSH14 9.939091 0.26840532 geth
## 88 PUSH15 10.447273 0.32586059 geth
## 89 PUSH16 8.818182 0.50234894 geth
## 90 PUSH17 10.014545 0.66363941 geth
## 91 PUSH18 10.110909 0.53693139 geth
## 92 PUSH19 10.787273 0.21256318 geth
## 93 PUSH20 9.968182 0.19637088 geth
## 94 PUSH21 10.198182 0.31579131 geth
## 95 PUSH22 11.531818 0.30135217 geth
## 96 PUSH23 11.699091 0.34922175 geth
## 97 PUSH24 10.576364 0.48727650 geth
## 98 PUSH25 9.968182 0.27626244 geth
## 99 PUSH26 10.360000 0.19707783 geth
## 100 PUSH27 9.917273 0.22150645 geth
## 101 PUSH28 10.272727 0.32293421 geth
## 102 PUSH29 10.369091 0.28390301 geth
## 103 PUSH30 10.496364 0.62398082 geth
## 104 PUSH31 11.196364 0.20737637 geth
## 105 PUSH32 10.728182 0.18510638 geth
## 106 DUP1 5.751818 0.15266563 geth
## 107 DUP2 5.595455 0.09854544 geth
## 108 DUP3 5.303636 0.51241914 geth
## 109 DUP4 5.192727 0.56996738 geth
## 110 DUP5 4.596364 0.61965096 geth
## 111 DUP6 5.766364 0.20685475 geth
## 112 DUP7 5.867273 0.21660711 geth
## 113 DUP8 6.043636 0.17465590 geth
## 114 DUP9 6.154545 0.32172914 geth
## 115 DUP10 5.941818 0.30320964 geth
## 116 DUP11 6.428182 0.68779328 geth
## 117 DUP12 5.975455 0.59110416 geth
## 118 DUP13 5.477273 0.10260832 geth
## 119 DUP14 5.689091 0.18698615 geth
## 120 DUP15 5.720000 0.17719786 geth
## 121 DUP16 6.559091 0.94966405 geth
## 122 SWAP1 6.424545 0.50522622 geth
## 123 SWAP2 6.217273 0.12882139 geth
## 124 SWAP3 6.181818 0.14070529 geth
## 125 SWAP4 7.347273 0.57817165 geth
## 126 SWAP5 5.833636 0.15655585 geth
## 127 SWAP6 5.820909 0.37275080 geth
## 128 SWAP7 6.112727 0.92554027 geth
## 129 SWAP8 5.881818 0.58024725 geth
## 130 SWAP9 13.953636 2.07600748 geth
## 131 SWAP10 4.120909 0.74979710 geth
## 132 SWAP11 5.113636 1.07671724 geth
## 133 SWAP12 6.487273 0.30956351 geth
## 134 SWAP13 7.237273 0.82556595 geth
## 135 SWAP14 5.831818 0.52400546 geth
## 136 SWAP15 6.023636 0.11961984 geth
## 137 SWAP16 6.059091 0.12227376 gethThe results are exported to reports-08.11.2024/estimated_cost_geth_full.csv.
if (params$output_estimated_cost != "") {
write.csv(estimates, params$output_estimated_cost, quote=FALSE, row.names=FALSE)
}